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Record W2964771088 · doi:10.1101/702001

Immune landscapes predict chemotherapy resistance and immunotherapy response in acute myeloid leukemia

2019· preprint· en· W2964771088 on OpenAlexaff
Jayakumar Vadakekolathu, Mark D. Minden, Tressa Hood, S. Church, Stephen Reeder, Heidi Altmann, Amy Sullivan, Elena Viboch, Tasleema Patel, Narmin Ibrahimova, Sarah Warren, Andrea Arruda, Yan Liang, John Muth, Marc Schmitz, Alessandra Cesano, A. Graham Pockley, Peter J.M. Valk, Bob Löwenberg, Martin Bornhäuser, Sarah K. Tasian, Michael P. Rettig, Jan Davidson‐Moncada, John F. DiPersio, Sergio Rutella

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2019
Typepreprint
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsPrincess Margaret Cancer Centre
FundersQatar National Research FundJohn and Lucille Van Geest FoundationFonds National de la Recherche Luxembourg
KeywordsImmunotherapyMyeloid leukemiaImmune systemImmunologyMyeloidChemotherapyDiseaseLeukemiaBone marrowMedicineBiologyCancer researchInternal medicine

Abstract

fetched live from OpenAlex

Abstract This study dissected the complexity of the immune architecture of acute myeloid leukemia (AML) at high resolution and assessed its influence on therapeutic response. Using 387 primary bone marrow samples from three discovery cohorts of children and adults with AML, we defined immune-infiltrated and immune-depleted disease subtypes and unraveled critical differences in immune gene expression across age groups and disease stages. Importantly, interferon (IFN)-γ-related mRNA profiles were predictive for both chemotherapy resistance and response of primary refractory/relapsed AML to flotetuzumab immunotherapy. Our compendium of microenvironmental gene and protein profiles sheds novel insights into the immuno-biology of AML and will inform the delivery of personalized immunotherapies to IFN-γ-dominant AML subtypes.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.784
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.244
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations28
Published2019
Admission routes1
Has abstractyes

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